{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a05bbab-7a5e-4a67-8dfa-bdae1d85fd18",
   "metadata": {},
   "source": [
    "Chapter 11\n",
    "# 用Seaborn生成热图\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "098abd6e-0bec-4b99-850e-7b70fa4b6271",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入包\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4769a8d-55e6-4b3a-b76a-d4766035a4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从seaborn中导入鸢尾花样本数据\n",
    "iris_sns = sns.load_dataset(\"iris\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d24efb7b-079b-4be2-9dce-4e18336ee68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制热图\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.heatmap(data=iris_sns.iloc[:,0:-1],\n",
    "            vmin = 0, vmax = 8,\n",
    "            ax = ax,\n",
    "            yticklabels = False,\n",
    "            xticklabels = ['Sepal length', 'Sepal width', \n",
    "                           'Petal length', 'Petal width'],\n",
    "            cmap = 'RdYlBu_r')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
